Nowadays blogging is emerging as a new way of building human-to-human relationship by offering personal information and sharing opinions with others on their own initiatives. These blogging activities would be also useful when people are in a movement surrounding lots of objects, such as products and shops, which are potentially interesting to them. However, immediate response from the objects is not available using legacy blogging systems. This is mainly due to the segregation of the blogging content owners from the objects. This paper, hence, aims to propose a methodology which allows the users to directly communicate with the objects in a timely and context-aware manner, not the object owners, to get useful information or get served immediately. To do so, the concept of artificial bloggers which work on behalf of the objects and eventually the object owners is proposed. The functionalities of u-Blogging are also introduced with prototype system.

The objective of this study is to forecast the operational continuous ability using Artificial Neural Networks in battalion defensive operation for the commander decision making support. The forecasting of the combat result is one of the most complex issue in military science. However, it is difficult to formulate a mathematical model to evaluate the combat power of a battalion in defensive operation since there are so many parameters and high temporal and spatial variability among variables. So in this study, we used company combat power level data in Battalion Command in Battle Training as input data and used Feed-Forward Multilayer Perceptrons(MLP) and General Regression Neural Network (GRNN) to evaluate operational continuous ability. The results show 82.62%, 85.48% of forecasting ability in spite of non-linear interactions among variables. We think that GRNN is a suitable technique for real-time commander's decision making and evaluation of the commitment priority of troops in reserve.

U-Publication, the Tag-Embedded publication, is one of U-Media. U-Media is defined as a media where human creates and consumes content through not only human cognitive and perceptual processes but also through the interactions between surrounding digital systems. U-Media provides information by generating, collecting, and attaching the content itself and the related information based on the interaction of the bio-systems incorporating digital information and devices embedded in humans, and surrounding objects including external digital devices. Using U-Publication, readers consume its content not only in offline but also online through a mobile RFID reader which touches and connects the URLs embedded in the RFID tags attached to it. Readers can consume the additional content though the hyperlinks attached to U-Publication and perform commercial activity as well as consumer the printed content. This paper defines the RFID-Tagged publication, proposes its related business models, and evaluates the alternative business models through a simulation study.

In this paper, we propose an agent architecture called L-CAA that is quite effective in real-time dynamic environments. L-CAA is an extension of CAA, the behavior-based agent architecture which was also developed by our research group. In order to improve adaptability to the changing environment, it is extended by adding reinforcement learning capability. To obtain stable performance, however, behavior selection and execution in the L-CAA architecture do not entirely rely on learning. In L-CAA, learning is utilized merely as a complimentary means for behavior selection and execution. Behavior selection mechanism in this architecture consists of two phases. In the first phase, the behaviors are extracted from the behavior library by checking the user-defined applicable conditions and utility of each behavior. If multiple behaviors are extracted in the first phase, the single behavior is selected to execute in the help of reinforcement learning in the second phase. That is, the behavior with the highest expected reward is selected by comparing Q values of individual behaviors updated through reinforcement learning. L-CAA can monitor the maintainable conditions of the executing behavior and stop immediately the behavior when some of the conditions fail due to dynamic change of the environment. Additionally, L-CAA can suspend and then resume the current behavior whenever it encounters a higher utility behavior. In order to analyze effectiveness of the L-CAA architecture, we implement an L-CAA-enabled agent autonomously playing in an Unreal Tournament game that is a well-known dynamic virtual environment, and then conduct several experiments using it.

This study presents a method for estimating the productivity of the ship operation in container terminal. The productivity of the ship operation is influenced by the specifications of each piece of equipment and layouts of the terminal, and the operational strategies. The handling equipments considered in this study are QC(Quay Crane), RMGC (Rail mounted gantry crane), and Transporter (TR). The simulation experiments are conducted to estimate the QC productivity based on the change of the design factors.

Recently, commerce paradigm is developing to e-commerce, mobile commerce, and ubiquitous commerce(u-commerce). While many companies consider to adopt u-commerce, they have a task to solve this problem. The typical consideration is to derive the critical success factors for u-commerce. By the literature survey, this paper suggests the critical success factors for e-commerce business and off-line business to transform to u-commerce environment. We find significant variables to contribute the management performance by analyzing the cause and effect relationship.

Ubiquitous Smart Space(USS) like u-City has been expected to create a high added value. However, developing USS has a high risk because it should use future technologies and development methodologies that have been never tried in the past. Hence, it has to be considered thoroughly in the very first stage of development. Moreover, USS usually uses several ubiquitous computing technologies combinationally because of the nature of USS. Despite of this, existing technology selection methodologies or technology evaluation methodologies only focus on a single technology. This leads us to develop a methodology of optimal technology combination for developing a specific USS. The purpose of this paper is to propose the methodology and to apply it to develop a real USS. We use portfolio theory and constraint satisfaction problem to determine an optimal technology combination. We also apply our methodology to the national ubiquitous computing project which carries out at present to validate it.

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. In order to solve the data imbalance problem, there has been proposed a number of techniques based on re-sampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.